Results 181 to 190 of about 3,388 (214)
Computers and Operations Research, 2008 zbMATH Open Web Interface contents unavailable due to conflicting licenses.
Stanislav Busygin +2 more
exaly +4 more sources
Some of the next articles are maybe not open access.
Related searches:
Related searches:
Topological biclustering ARTMAP for identifying within bicluster relationships
Neural Networks, 2023Biclustering is a powerful tool for exploratory data analysis in domains such as social networking, data reduction, and differential gene expression studies. Topological learning identifies connected regions that are difficult to find using other traditional clustering methods and produces a graphical representation.
Raghu Yelugam +2 more
openaire +2 more sources
Biclustering with missing data
Information Sciences, 2020Abstract Biclustering is a statistical learning methodology that simultaneously partitions rows and columns of a rectangular data array into homogeneous subsets. Biclustering is known to be an NP-hard problem, and therefore various heuristic approaches have been proposed.
S Olafsson
exaly +2 more sources
Biclustering on expression data: A review [PDF]
Journal of Biomedical Informatics, 2015 Biclustering has become a popular technique for the study of gene expression data, especially for discovering functionally related gene sets under different subsets of experimental conditions.
BEATRIZ Pontes +2 more
exaly +2 more sources
2011
The search for similarities in large data sets has a very important role in many scientific fields. It permits to classify several types of data without an explicit information about it. In many cases researchers use analysis methodologies such as clustering to classify data with respect to the patterns and conditions together.
Ekaterina Nosova +3 more
openaire +4 more sources
The search for similarities in large data sets has a very important role in many scientific fields. It permits to classify several types of data without an explicit information about it. In many cases researchers use analysis methodologies such as clustering to classify data with respect to the patterns and conditions together.
Ekaterina Nosova +3 more
openaire +4 more sources
Assessing the quality of biclusters using fuzzy biclustering index
International Journal of Data Mining and Bioinformatics, 2016Several algorithms are proposed in the literature for extracting local patterns from a large data matrix. This technique of data mining is known as biclustering. Each of the biclustering algorithms is specialised in extracting different kinds of biclusters. Some algorithms detect equal biclusters, whereas some identify scaled biclusters Madeira et al.,
Nishchal K. Verma +2 more
openaire +1 more source
Biclustering of Biological Sequences
2017 28th International Workshop on Database and Expert Systems Applications (DEXA), 2017The analysis of biological data is a challenging problem in bioinformatics and data mining field. Given the complexity of the analysis of biological information, several methods have been proposed for analyzing this biological information in databases mostly in the form of genetic sequences and protein structures.
Faouzi Mhamdi, Sourour Marai
openaire +1 more source
GFBA: A Biclustering Algorithm for Discovering Value-Coherent Biclusters
2007Clustering has been one of the most popular approaches used in gene expression data analysis. A clustering method is typically used to partition genes according to their similarity of expression under different conditions. However, it is often the case that some genes behave similarly only on a subset of conditions and their behavior is uncorrelated ...
Xubo Fei +3 more
openaire +1 more source
Biclustering with a quantum annealer
Soft Computing, 2018zbMATH Open Web Interface contents unavailable due to conflicting licenses.
Bottarelli, Lorenzo +5 more
openaire +1 more source
BAYESIAN BICLUSTERING FOR PATIENT STRATIFICATION [PDF]
Biocomputing 2016, 2015 The move from Empirical Medicine towards Personalized Medicine has attracted attention to Stratified Medicine (SM). Some methods are provided in the literature for patient stratification, which is the central task of SM, however, there are still significant open issues.
Sahand Khakabimamaghani, Martin Ester
openaire +2 more sources